def active_representation_learning_experiment(variant): import rlkit.torch.pytorch_util as ptu from rlkit.data_management.obs_dict_replay_buffer import ObsDictReplayBuffer from rlkit.torch.networks import ConcatMlp from rlkit.torch.sac.policies import TanhGaussianPolicy from rlkit.torch.arl.active_representation_learning_algorithm import \ ActiveRepresentationLearningAlgorithm from rlkit.torch.arl.representation_wrappers import RepresentationWrappedEnv from multiworld.core.image_env import ImageEnv from rlkit.samplers.data_collector import MdpPathCollector preprocess_rl_variant(variant) model_class = variant.get('model_class') model_kwargs = variant.get('model_kwargs') model = model_class(**model_kwargs) model.representation_size = 4 model.imsize = 48 variant["vae_path"] = model reward_params = variant.get("reward_params", dict()) init_camera = variant.get("init_camera", None) env = variant["env_class"](**variant['env_kwargs']) image_env = ImageEnv( env, variant.get('imsize'), init_camera=init_camera, transpose=True, normalize=True, ) env = RepresentationWrappedEnv( image_env, model, ) uniform_dataset_fn = variant.get('generate_uniform_dataset_fn', None) if uniform_dataset_fn: uniform_dataset = uniform_dataset_fn( **variant['generate_uniform_dataset_kwargs']) else: uniform_dataset = None observation_key = variant.get('observation_key', 'latent_observation') desired_goal_key = variant.get('desired_goal_key', 'latent_desired_goal') achieved_goal_key = desired_goal_key.replace("desired", "achieved") obs_dim = env.observation_space.spaces[observation_key].low.size action_dim = env.action_space.low.size hidden_sizes = variant.get('hidden_sizes', [400, 300]) qf1 = ConcatMlp( input_size=obs_dim + action_dim, output_size=1, hidden_sizes=hidden_sizes, ) qf2 = ConcatMlp( input_size=obs_dim + action_dim, output_size=1, hidden_sizes=hidden_sizes, ) target_qf1 = ConcatMlp( input_size=obs_dim + action_dim, output_size=1, hidden_sizes=hidden_sizes, ) target_qf2 = ConcatMlp( input_size=obs_dim + action_dim, output_size=1, hidden_sizes=hidden_sizes, ) policy = TanhGaussianPolicy( obs_dim=obs_dim, action_dim=action_dim, hidden_sizes=hidden_sizes, ) vae = env.vae replay_buffer = ObsDictReplayBuffer(env=env, **variant['replay_buffer_kwargs']) model_trainer_class = variant.get('model_trainer_class') model_trainer_kwargs = variant.get('model_trainer_kwargs') model_trainer = model_trainer_class( model, **model_trainer_kwargs, ) # vae_trainer = ConvVAETrainer( # env.vae, # **variant['online_vae_trainer_kwargs'] # ) assert 'vae_training_schedule' not in variant, "Just put it in algo_kwargs" max_path_length = variant['max_path_length'] trainer = SACTrainer(env=env, policy=policy, qf1=qf1, qf2=qf2, target_qf1=target_qf1, target_qf2=target_qf2, **variant['twin_sac_trainer_kwargs']) # trainer = HERTrainer(trainer) eval_path_collector = MdpPathCollector( env, MakeDeterministic(policy), # max_path_length, # observation_key=observation_key, # desired_goal_key=desired_goal_key, ) expl_path_collector = MdpPathCollector( env, policy, # max_path_length, # observation_key=observation_key, # desired_goal_key=desired_goal_key, ) algorithm = ActiveRepresentationLearningAlgorithm( trainer=trainer, exploration_env=env, evaluation_env=env, exploration_data_collector=expl_path_collector, evaluation_data_collector=eval_path_collector, replay_buffer=replay_buffer, model=model, model_trainer=model_trainer, uniform_dataset=uniform_dataset, max_path_length=max_path_length, **variant['algo_kwargs']) algorithm.to(ptu.device) vae.to(ptu.device) algorithm.train()
def tdm_td3_experiment_online_vae(variant): import rlkit.samplers.rollout_functions as rf import rlkit.torch.pytorch_util as ptu from rlkit.data_management.online_vae_replay_buffer import \ OnlineVaeRelabelingBuffer from rlkit.exploration_strategies.base import ( PolicyWrappedWithExplorationStrategy) from rlkit.state_distance.tdm_networks import TdmQf, TdmPolicy from rlkit.torch.vae.vae_trainer import ConvVAETrainer from rlkit.torch.online_vae.online_vae_tdm_td3 import OnlineVaeTdmTd3 preprocess_rl_variant(variant) env = get_envs(variant) es = get_exploration_strategy(variant, env) vae_trainer_kwargs = variant.get('vae_trainer_kwargs') observation_key = variant.get('observation_key', 'latent_observation') desired_goal_key = variant.get('desired_goal_key', 'latent_desired_goal') achieved_goal_key = desired_goal_key.replace("desired", "achieved") obs_dim = (env.observation_space.spaces[observation_key].low.size) goal_dim = (env.observation_space.spaces[desired_goal_key].low.size) action_dim = env.action_space.low.size vectorized = 'vectorized' in env.reward_type variant['algo_kwargs']['tdm_td3_kwargs']['tdm_kwargs'][ 'vectorized'] = vectorized norm_order = env.norm_order # variant['algo_kwargs']['tdm_td3_kwargs']['tdm_kwargs'][ # 'norm_order'] = norm_order qf1 = TdmQf(env=env, vectorized=vectorized, norm_order=norm_order, observation_dim=obs_dim, goal_dim=goal_dim, action_dim=action_dim, **variant['qf_kwargs']) qf2 = TdmQf(env=env, vectorized=vectorized, norm_order=norm_order, observation_dim=obs_dim, goal_dim=goal_dim, action_dim=action_dim, **variant['qf_kwargs']) policy = TdmPolicy(env=env, observation_dim=obs_dim, goal_dim=goal_dim, action_dim=action_dim, **variant['policy_kwargs']) exploration_policy = PolicyWrappedWithExplorationStrategy( exploration_strategy=es, policy=policy, ) vae = env.vae replay_buffer = OnlineVaeRelabelingBuffer( vae=vae, env=env, observation_key=observation_key, desired_goal_key=desired_goal_key, achieved_goal_key=achieved_goal_key, **variant['replay_buffer_kwargs']) algo_kwargs = variant['algo_kwargs']['tdm_td3_kwargs'] td3_kwargs = algo_kwargs['td3_kwargs'] td3_kwargs['training_env'] = env tdm_kwargs = algo_kwargs['tdm_kwargs'] tdm_kwargs['observation_key'] = observation_key tdm_kwargs['desired_goal_key'] = desired_goal_key algo_kwargs["replay_buffer"] = replay_buffer t = ConvVAETrainer(variant['vae_train_data'], variant['vae_test_data'], vae, beta=variant['online_vae_beta'], **vae_trainer_kwargs) render = variant["render"] assert 'vae_training_schedule' not in variant, "Just put it in algo_kwargs" algorithm = OnlineVaeTdmTd3( online_vae_kwargs=dict(vae=vae, vae_trainer=t, **variant['algo_kwargs']['online_vae_kwargs']), tdm_td3_kwargs=dict(env=env, qf1=qf1, qf2=qf2, policy=policy, exploration_policy=exploration_policy, **variant['algo_kwargs']['tdm_td3_kwargs']), ) algorithm.to(ptu.device) vae.to(ptu.device) if variant.get("save_video", True): policy.train(False) rollout_function = rf.create_rollout_function( rf.tdm_rollout, init_tau=algorithm._sample_max_tau_for_rollout(), decrement_tau=algorithm.cycle_taus_for_rollout, cycle_tau=algorithm.cycle_taus_for_rollout, max_path_length=algorithm.max_path_length, observation_key=algorithm.observation_key, desired_goal_key=algorithm.desired_goal_key, ) video_func = get_video_save_func( rollout_function, env, algorithm.eval_policy, variant, ) algorithm.post_train_funcs.append(video_func) algorithm.to(ptu.device) if not variant.get("do_state_exp", False): env.vae.to(ptu.device) algorithm.train()
def tdm_twin_sac_experiment(variant): import rlkit.samplers.rollout_functions as rf import rlkit.torch.pytorch_util as ptu from rlkit.data_management.obs_dict_replay_buffer import \ ObsDictRelabelingBuffer from rlkit.state_distance.tdm_networks import ( TdmQf, TdmVf, StochasticTdmPolicy, ) from rlkit.state_distance.tdm_twin_sac import TdmTwinSAC preprocess_rl_variant(variant) env = get_envs(variant) observation_key = variant.get('observation_key', 'latent_observation') desired_goal_key = variant.get('desired_goal_key', 'latent_desired_goal') achieved_goal_key = desired_goal_key.replace("desired", "achieved") obs_dim = (env.observation_space.spaces[observation_key].low.size) goal_dim = (env.observation_space.spaces[desired_goal_key].low.size) action_dim = env.action_space.low.size vectorized = 'vectorized' in env.reward_type norm_order = env.norm_order variant['algo_kwargs']['tdm_kwargs']['vectorized'] = vectorized variant['qf_kwargs']['vectorized'] = vectorized variant['vf_kwargs']['vectorized'] = vectorized variant['qf_kwargs']['norm_order'] = norm_order variant['vf_kwargs']['norm_order'] = norm_order qf1 = TdmQf(env=env, observation_dim=obs_dim, goal_dim=goal_dim, action_dim=action_dim, **variant['qf_kwargs']) qf2 = TdmQf(env=env, observation_dim=obs_dim, goal_dim=goal_dim, action_dim=action_dim, **variant['qf_kwargs']) vf = TdmVf(env=env, observation_dim=obs_dim, goal_dim=goal_dim, **variant['vf_kwargs']) policy = StochasticTdmPolicy(env=env, observation_dim=obs_dim, goal_dim=goal_dim, action_dim=action_dim, **variant['policy_kwargs']) variant['replay_buffer_kwargs']['vectorized'] = vectorized replay_buffer = ObsDictRelabelingBuffer( env=env, observation_key=observation_key, desired_goal_key=desired_goal_key, achieved_goal_key=achieved_goal_key, **variant['replay_buffer_kwargs']) algo_kwargs = variant['algo_kwargs'] algo_kwargs['replay_buffer'] = replay_buffer base_kwargs = algo_kwargs['base_kwargs'] base_kwargs['training_env'] = env base_kwargs['render'] = variant["render"] base_kwargs['render_during_eval'] = variant["render"] tdm_kwargs = algo_kwargs['tdm_kwargs'] tdm_kwargs['observation_key'] = observation_key tdm_kwargs['desired_goal_key'] = desired_goal_key algorithm = TdmTwinSAC(env, qf1=qf1, qf2=qf2, vf=vf, policy=policy, **variant['algo_kwargs']) if variant.get("save_video", True): rollout_function = rf.create_rollout_function( rf.tdm_rollout, init_tau=algorithm._sample_max_tau_for_rollout(), decrement_tau=algorithm.cycle_taus_for_rollout, cycle_tau=algorithm.cycle_taus_for_rollout, max_path_length=algorithm.max_path_length, observation_key=algorithm.observation_key, desired_goal_key=algorithm.desired_goal_key, ) video_func = get_video_save_func( rollout_function, env, algorithm.eval_policy, variant, ) algorithm.post_train_funcs.append(video_func) algorithm.to(ptu.device) if not variant.get("do_state_exp", False): env.vae.to(ptu.device) algorithm.train()
def tdm_td3_experiment(variant): import rlkit.samplers.rollout_functions as rf import rlkit.torch.pytorch_util as ptu from rlkit.core import logger from rlkit.data_management.obs_dict_replay_buffer import \ ObsDictRelabelingBuffer from rlkit.exploration_strategies.base import ( PolicyWrappedWithExplorationStrategy) from rlkit.state_distance.tdm_networks import TdmQf, TdmPolicy from rlkit.state_distance.tdm_td3 import TdmTd3 preprocess_rl_variant(variant) env = get_envs(variant) es = get_exploration_strategy(variant, env) observation_key = variant.get('observation_key', 'latent_observation') desired_goal_key = variant.get('desired_goal_key', 'latent_desired_goal') achieved_goal_key = desired_goal_key.replace("desired", "achieved") obs_dim = (env.observation_space.spaces[observation_key].low.size) goal_dim = (env.observation_space.spaces[desired_goal_key].low.size) action_dim = env.action_space.low.size vectorized = 'vectorized' in env.reward_type norm_order = env.norm_order variant['algo_kwargs']['tdm_kwargs']['vectorized'] = vectorized variant['qf_kwargs']['vectorized'] = vectorized variant['qf_kwargs']['norm_order'] = norm_order qf1 = TdmQf(env=env, observation_dim=obs_dim, goal_dim=goal_dim, action_dim=action_dim, **variant['qf_kwargs']) qf2 = TdmQf(env=env, observation_dim=obs_dim, goal_dim=goal_dim, action_dim=action_dim, **variant['qf_kwargs']) policy = TdmPolicy(env=env, observation_dim=obs_dim, goal_dim=goal_dim, action_dim=action_dim, **variant['policy_kwargs']) exploration_policy = PolicyWrappedWithExplorationStrategy( exploration_strategy=es, policy=policy, ) variant['replay_buffer_kwargs']['vectorized'] = vectorized replay_buffer = ObsDictRelabelingBuffer( env=env, observation_key=observation_key, desired_goal_key=desired_goal_key, achieved_goal_key=achieved_goal_key, **variant['replay_buffer_kwargs']) algo_kwargs = variant['algo_kwargs'] algo_kwargs['replay_buffer'] = replay_buffer base_kwargs = algo_kwargs['base_kwargs'] base_kwargs['training_env'] = env base_kwargs['render'] = variant["render"] base_kwargs['render_during_eval'] = variant["render"] tdm_kwargs = algo_kwargs['tdm_kwargs'] tdm_kwargs['observation_key'] = observation_key tdm_kwargs['desired_goal_key'] = desired_goal_key algorithm = TdmTd3(env, qf1=qf1, qf2=qf2, policy=policy, exploration_policy=exploration_policy, **variant['algo_kwargs']) algorithm.to(ptu.device) if not variant.get("do_state_exp", False): env.vae.to(ptu.device) if variant.get("save_video", True): logdir = logger.get_snapshot_dir() policy.train(False) rollout_function = rf.create_rollout_function( rf.tdm_rollout, init_tau=algorithm.max_tau, max_path_length=algorithm.max_path_length, observation_key=algorithm.observation_key, desired_goal_key=algorithm.desired_goal_key, ) video_func = get_video_save_func( rollout_function, env, policy, variant, ) algorithm.post_train_funcs.append(video_func) algorithm.train()
def td3_experiment_offpolicy_online_vae(variant): import rlkit.torch.pytorch_util as ptu from rlkit.data_management.online_vae_replay_buffer import \ OnlineVaeRelabelingBuffer from rlkit.torch.networks import ConcatMlp, TanhMlpPolicy from rlkit.torch.vae.vae_trainer import ConvVAETrainer from rlkit.torch.td3.td3 import TD3 from rlkit.exploration_strategies.base import ( PolicyWrappedWithExplorationStrategy) from rlkit.exploration_strategies.gaussian_and_epislon import \ GaussianAndEpislonStrategy from rlkit.torch.vae.online_vae_offpolicy_algorithm import OnlineVaeOffpolicyAlgorithm preprocess_rl_variant(variant) env = get_envs(variant) uniform_dataset_fn = variant.get('generate_uniform_dataset_fn', None) if uniform_dataset_fn: uniform_dataset = uniform_dataset_fn( **variant['generate_uniform_dataset_kwargs']) else: uniform_dataset = None observation_key = variant.get('observation_key', 'latent_observation') desired_goal_key = variant.get('desired_goal_key', 'latent_desired_goal') achieved_goal_key = desired_goal_key.replace("desired", "achieved") obs_dim = (env.observation_space.spaces[observation_key].low.size + env.observation_space.spaces[desired_goal_key].low.size) action_dim = env.action_space.low.size hidden_sizes = variant.get('hidden_sizes', [400, 300]) qf1 = ConcatMlp( input_size=obs_dim + action_dim, output_size=1, hidden_sizes=hidden_sizes, ) qf2 = ConcatMlp( input_size=obs_dim + action_dim, output_size=1, hidden_sizes=hidden_sizes, ) target_qf1 = ConcatMlp( input_size=obs_dim + action_dim, output_size=1, hidden_sizes=hidden_sizes, ) target_qf2 = ConcatMlp( input_size=obs_dim + action_dim, output_size=1, hidden_sizes=hidden_sizes, ) policy = TanhMlpPolicy( input_size=obs_dim, output_size=action_dim, hidden_sizes=hidden_sizes, # **variant['policy_kwargs'] ) target_policy = TanhMlpPolicy( input_size=obs_dim, output_size=action_dim, hidden_sizes=hidden_sizes, # **variant['policy_kwargs'] ) es = GaussianAndEpislonStrategy( action_space=env.action_space, max_sigma=.2, min_sigma=.2, # constant sigma epsilon=.3, ) expl_policy = PolicyWrappedWithExplorationStrategy( exploration_strategy=es, policy=policy, ) vae = env.vae replay_buffer_class = variant.get("replay_buffer_class", OnlineVaeRelabelingBuffer) replay_buffer = replay_buffer_class(vae=env.vae, env=env, observation_key=observation_key, desired_goal_key=desired_goal_key, achieved_goal_key=achieved_goal_key, **variant['replay_buffer_kwargs']) replay_buffer.representation_size = vae.representation_size vae_trainer_class = variant.get("vae_trainer_class", ConvVAETrainer) vae_trainer = vae_trainer_class(env.vae, **variant['online_vae_trainer_kwargs']) assert 'vae_training_schedule' not in variant, "Just put it in algo_kwargs" max_path_length = variant['max_path_length'] trainer = TD3(policy=policy, qf1=qf1, qf2=qf2, target_qf1=target_qf1, target_qf2=target_qf2, target_policy=target_policy, **variant['td3_trainer_kwargs']) trainer = HERTrainer(trainer) eval_path_collector = VAEWrappedEnvPathCollector( variant['evaluation_goal_sampling_mode'], env, policy, max_path_length, observation_key=observation_key, desired_goal_key=desired_goal_key, ) expl_path_collector = VAEWrappedEnvPathCollector( variant['exploration_goal_sampling_mode'], env, expl_policy, max_path_length, observation_key=observation_key, desired_goal_key=desired_goal_key, ) algorithm = OnlineVaeOffpolicyAlgorithm( trainer=trainer, exploration_env=env, evaluation_env=env, exploration_data_collector=expl_path_collector, evaluation_data_collector=eval_path_collector, replay_buffer=replay_buffer, vae=vae, vae_trainer=vae_trainer, uniform_dataset=uniform_dataset, max_path_length=max_path_length, **variant['algo_kwargs']) if variant.get("save_video", True): video_func = VideoSaveFunction( env, variant, ) algorithm.post_train_funcs.append(video_func) if variant['custom_goal_sampler'] == 'replay_buffer': env.custom_goal_sampler = replay_buffer.sample_buffer_goals algorithm.to(ptu.device) vae.to(ptu.device) algorithm.pretrain() algorithm.train()
def td3_experiment_online_vae_exploring(variant): import rlkit.samplers.rollout_functions as rf import rlkit.torch.pytorch_util as ptu from rlkit.data_management.online_vae_replay_buffer import \ OnlineVaeRelabelingBuffer from rlkit.exploration_strategies.base import ( PolicyWrappedWithExplorationStrategy) from rlkit.torch.her.online_vae_joint_algo import OnlineVaeHerJointAlgo from rlkit.torch.networks import ConcatMlp, TanhMlpPolicy from rlkit.torch.td3.td3 import TD3 from rlkit.torch.vae.vae_trainer import ConvVAETrainer preprocess_rl_variant(variant) env = get_envs(variant) es = get_exploration_strategy(variant, env) observation_key = variant.get('observation_key', 'latent_observation') desired_goal_key = variant.get('desired_goal_key', 'latent_desired_goal') achieved_goal_key = desired_goal_key.replace("desired", "achieved") obs_dim = (env.observation_space.spaces[observation_key].low.size + env.observation_space.spaces[desired_goal_key].low.size) action_dim = env.action_space.low.size qf1 = ConcatMlp( input_size=obs_dim + action_dim, output_size=1, **variant['qf_kwargs'], ) qf2 = ConcatMlp( input_size=obs_dim + action_dim, output_size=1, **variant['qf_kwargs'], ) policy = TanhMlpPolicy( input_size=obs_dim, output_size=action_dim, **variant['policy_kwargs'], ) exploration_policy = PolicyWrappedWithExplorationStrategy( exploration_strategy=es, policy=policy, ) exploring_qf1 = ConcatMlp( input_size=obs_dim + action_dim, output_size=1, **variant['qf_kwargs'], ) exploring_qf2 = ConcatMlp( input_size=obs_dim + action_dim, output_size=1, **variant['qf_kwargs'], ) exploring_policy = TanhMlpPolicy( input_size=obs_dim, output_size=action_dim, **variant['policy_kwargs'], ) exploring_exploration_policy = PolicyWrappedWithExplorationStrategy( exploration_strategy=es, policy=exploring_policy, ) vae = env.vae replay_buffer = OnlineVaeRelabelingBuffer( vae=vae, env=env, observation_key=observation_key, desired_goal_key=desired_goal_key, achieved_goal_key=achieved_goal_key, **variant['replay_buffer_kwargs']) variant["algo_kwargs"]["replay_buffer"] = replay_buffer if variant.get('use_replay_buffer_goals', False): env.replay_buffer = replay_buffer env.use_replay_buffer_goals = True vae_trainer_kwargs = variant.get('vae_trainer_kwargs') t = ConvVAETrainer(variant['vae_train_data'], variant['vae_test_data'], vae, beta=variant['online_vae_beta'], **vae_trainer_kwargs) control_algorithm = TD3(env=env, training_env=env, qf1=qf1, qf2=qf2, policy=policy, exploration_policy=exploration_policy, **variant['algo_kwargs']) exploring_algorithm = TD3(env=env, training_env=env, qf1=exploring_qf1, qf2=exploring_qf2, policy=exploring_policy, exploration_policy=exploring_exploration_policy, **variant['algo_kwargs']) assert 'vae_training_schedule' not in variant,\ "Just put it in joint_algo_kwargs" algorithm = OnlineVaeHerJointAlgo(vae=vae, vae_trainer=t, env=env, training_env=env, policy=policy, exploration_policy=exploration_policy, replay_buffer=replay_buffer, algo1=control_algorithm, algo2=exploring_algorithm, algo1_prefix="Control_", algo2_prefix="VAE_Exploration_", observation_key=observation_key, desired_goal_key=desired_goal_key, **variant['joint_algo_kwargs']) algorithm.to(ptu.device) vae.to(ptu.device) if variant.get("save_video", True): policy.train(False) rollout_function = rf.create_rollout_function( rf.multitask_rollout, max_path_length=algorithm.max_path_length, observation_key=algorithm.observation_key, desired_goal_key=algorithm.desired_goal_key, ) video_func = get_video_save_func( rollout_function, env, algorithm.eval_policy, variant, ) algorithm.post_train_funcs.append(video_func) algorithm.train()
def twin_sac_experiment_online_vae(variant): import rlkit.torch.pytorch_util as ptu from rlkit.data_management.online_vae_replay_buffer import \ OnlineVaeRelabelingBuffer from rlkit.torch.networks import ConcatMlp from rlkit.torch.sac.policies import TanhGaussianPolicy from rlkit.torch.vae.vae_trainer import ConvVAETrainer preprocess_rl_variant(variant) env = get_envs(variant) uniform_dataset_fn = variant.get('generate_uniform_dataset_fn', None) if uniform_dataset_fn: uniform_dataset = uniform_dataset_fn( **variant['generate_uniform_dataset_kwargs']) else: uniform_dataset = None observation_key = variant.get('observation_key', 'latent_observation') desired_goal_key = variant.get('desired_goal_key', 'latent_desired_goal') achieved_goal_key = desired_goal_key.replace("desired", "achieved") obs_dim = (env.observation_space.spaces[observation_key].low.size + env.observation_space.spaces[desired_goal_key].low.size) action_dim = env.action_space.low.size hidden_sizes = variant.get('hidden_sizes', [400, 300]) qf1 = ConcatMlp( input_size=obs_dim + action_dim, output_size=1, hidden_sizes=hidden_sizes, ) qf2 = ConcatMlp( input_size=obs_dim + action_dim, output_size=1, hidden_sizes=hidden_sizes, ) target_qf1 = ConcatMlp( input_size=obs_dim + action_dim, output_size=1, hidden_sizes=hidden_sizes, ) target_qf2 = ConcatMlp( input_size=obs_dim + action_dim, output_size=1, hidden_sizes=hidden_sizes, ) policy = TanhGaussianPolicy( obs_dim=obs_dim, action_dim=action_dim, hidden_sizes=hidden_sizes, ) vae = env.vae replay_buffer_class = variant.get("replay_buffer_class", OnlineVaeRelabelingBuffer) replay_buffer = replay_buffer_class(vae=env.vae, env=env, observation_key=observation_key, desired_goal_key=desired_goal_key, achieved_goal_key=achieved_goal_key, **variant['replay_buffer_kwargs']) vae_trainer_class = variant.get("vae_trainer_class", ConvVAETrainer) vae_trainer = vae_trainer_class(env.vae, **variant['online_vae_trainer_kwargs']) assert 'vae_training_schedule' not in variant, "Just put it in algo_kwargs" max_path_length = variant['max_path_length'] trainer = SACTrainer(env=env, policy=policy, qf1=qf1, qf2=qf2, target_qf1=target_qf1, target_qf2=target_qf2, **variant['twin_sac_trainer_kwargs']) trainer = HERTrainer(trainer) eval_path_collector = VAEWrappedEnvPathCollector( variant['evaluation_goal_sampling_mode'], env, MakeDeterministic(policy), max_path_length, observation_key=observation_key, desired_goal_key=desired_goal_key, ) expl_path_collector = VAEWrappedEnvPathCollector( variant['exploration_goal_sampling_mode'], env, policy, max_path_length, observation_key=observation_key, desired_goal_key=desired_goal_key, ) algorithm = OnlineVaeAlgorithm( trainer=trainer, exploration_env=env, evaluation_env=env, exploration_data_collector=expl_path_collector, evaluation_data_collector=eval_path_collector, replay_buffer=replay_buffer, vae=vae, vae_trainer=vae_trainer, uniform_dataset=uniform_dataset, max_path_length=max_path_length, **variant['algo_kwargs']) if variant.get("save_video", True): video_func = VideoSaveFunction( env, variant, ) algorithm.post_train_funcs.append(video_func) if variant['custom_goal_sampler'] == 'replay_buffer': env.custom_goal_sampler = replay_buffer.sample_buffer_goals algorithm.to(ptu.device) vae.to(ptu.device) algorithm.train()
def rl_context_experiment(variant): import rlkit.torch.pytorch_util as ptu from rlkit.torch.td3.td3 import TD3 as TD3Trainer from rlkit.torch.sac.sac import SACTrainer from rlkit.torch.torch_rl_algorithm import TorchBatchRLAlgorithm from rlkit.torch.networks import ConcatMlp, TanhMlpPolicy from rlkit.torch.sac.policies import TanhGaussianPolicy from rlkit.torch.sac.policies import MakeDeterministic preprocess_rl_variant(variant) max_path_length = variant['max_path_length'] observation_key = variant.get('observation_key', 'latent_observation') desired_goal_key = variant.get('desired_goal_key', 'latent_desired_goal') achieved_goal_key = variant.get('achieved_goal_key', 'latent_achieved_goal') contextual_mdp = variant.get('contextual_mdp', True) print("contextual_mdp:", contextual_mdp) mask_variant = variant.get('mask_variant', {}) mask_conditioned = mask_variant.get('mask_conditioned', False) print("mask_conditioned:", mask_conditioned) if mask_conditioned: assert contextual_mdp if 'sac' in variant['algorithm'].lower(): rl_algo = 'sac' elif 'td3' in variant['algorithm'].lower(): rl_algo = 'td3' else: raise NotImplementedError print("RL algorithm:", rl_algo) ### load the example dataset, if running checkpoints ### if 'ckpt' in variant: import os.path as osp example_set_variant = variant.get('example_set_variant', dict()) example_set_variant['use_cache'] = True example_set_variant['cache_path'] = osp.join(variant['ckpt'], 'example_dataset.npy') if mask_conditioned: env = get_envs(variant) mask_format = mask_variant['param_variant']['mask_format'] assert mask_format in ['vector', 'matrix', 'distribution', 'cond_distribution'] goal_dim = env.observation_space.spaces[desired_goal_key].low.size if mask_format in ['vector']: context_dim_for_networks = goal_dim + goal_dim elif mask_format in ['matrix', 'distribution', 'cond_distribution']: context_dim_for_networks = goal_dim + (goal_dim * goal_dim) else: raise TypeError if 'ckpt' in variant: from rlkit.misc.asset_loader import local_path_from_s3_or_local_path import os.path as osp filename = local_path_from_s3_or_local_path(osp.join(variant['ckpt'], 'masks.npy')) masks = np.load(filename, allow_pickle=True)[()] else: masks = get_mask_params( env=env, example_set_variant=variant['example_set_variant'], param_variant=mask_variant['param_variant'], ) mask_keys = list(masks.keys()) context_keys = [desired_goal_key] + mask_keys else: context_keys = [desired_goal_key] def contextual_env_distrib_and_reward(mode='expl'): assert mode in ['expl', 'eval'] env = get_envs(variant) if mode == 'expl': goal_sampling_mode = variant.get('expl_goal_sampling_mode', None) elif mode == 'eval': goal_sampling_mode = variant.get('eval_goal_sampling_mode', None) if goal_sampling_mode not in [None, 'example_set']: env.goal_sampling_mode = goal_sampling_mode mask_ids_for_training = mask_variant.get('mask_ids_for_training', None) if mask_conditioned: context_distrib = MaskDictDistribution( env, desired_goal_keys=[desired_goal_key], mask_format=mask_format, masks=masks, max_subtasks_to_focus_on=mask_variant.get('max_subtasks_to_focus_on', None), prev_subtask_weight=mask_variant.get('prev_subtask_weight', None), mask_distr=mask_variant.get('train_mask_distr', None), mask_ids=mask_ids_for_training, ) reward_fn = ContextualMaskingRewardFn( achieved_goal_from_observation=IndexIntoAchievedGoal(achieved_goal_key), desired_goal_key=desired_goal_key, achieved_goal_key=achieved_goal_key, mask_keys=mask_keys, mask_format=mask_format, use_g_for_mean=mask_variant['use_g_for_mean'], use_squared_reward=mask_variant.get('use_squared_reward', False), ) else: if goal_sampling_mode == 'example_set': example_dataset = gen_example_sets(get_envs(variant), variant['example_set_variant']) assert len(example_dataset['list_of_waypoints']) == 1 from rlkit.envs.contextual.set_distributions import GoalDictDistributionFromSet context_distrib = GoalDictDistributionFromSet( example_dataset['list_of_waypoints'][0], desired_goal_keys=[desired_goal_key], ) else: context_distrib = GoalDictDistributionFromMultitaskEnv( env, desired_goal_keys=[desired_goal_key], ) reward_fn = ContextualRewardFnFromMultitaskEnv( env=env, achieved_goal_from_observation=IndexIntoAchievedGoal(achieved_goal_key), desired_goal_key=desired_goal_key, achieved_goal_key=achieved_goal_key, additional_obs_keys=variant['contextual_replay_buffer_kwargs'].get('observation_keys', None), ) diag_fn = GoalConditionedDiagnosticsToContextualDiagnostics( env.goal_conditioned_diagnostics, desired_goal_key=desired_goal_key, observation_key=observation_key, ) env = ContextualEnv( env, context_distribution=context_distrib, reward_fn=reward_fn, observation_key=observation_key, contextual_diagnostics_fns=[diag_fn], update_env_info_fn=delete_info if not variant.get('keep_env_infos', False) else None, ) return env, context_distrib, reward_fn env, context_distrib, reward_fn = contextual_env_distrib_and_reward(mode='expl') eval_env, eval_context_distrib, _ = contextual_env_distrib_and_reward(mode='eval') if mask_conditioned: obs_dim = ( env.observation_space.spaces[observation_key].low.size + context_dim_for_networks ) elif contextual_mdp: obs_dim = ( env.observation_space.spaces[observation_key].low.size + env.observation_space.spaces[desired_goal_key].low.size ) else: obs_dim = env.observation_space.spaces[observation_key].low.size action_dim = env.action_space.low.size if 'ckpt' in variant and 'ckpt_epoch' in variant: from rlkit.misc.asset_loader import local_path_from_s3_or_local_path import os.path as osp ckpt_epoch = variant['ckpt_epoch'] if ckpt_epoch is not None: epoch = variant['ckpt_epoch'] filename = local_path_from_s3_or_local_path(osp.join(variant['ckpt'], 'itr_%d.pkl' % epoch)) else: filename = local_path_from_s3_or_local_path(osp.join(variant['ckpt'], 'params.pkl')) print("Loading ckpt from", filename) data = torch.load(filename) qf1 = data['trainer/qf1'] qf2 = data['trainer/qf2'] target_qf1 = data['trainer/target_qf1'] target_qf2 = data['trainer/target_qf2'] policy = data['trainer/policy'] eval_policy = data['evaluation/policy'] expl_policy = data['exploration/policy'] else: qf1 = ConcatMlp( input_size=obs_dim + action_dim, output_size=1, **variant['qf_kwargs'] ) qf2 = ConcatMlp( input_size=obs_dim + action_dim, output_size=1, **variant['qf_kwargs'] ) target_qf1 = ConcatMlp( input_size=obs_dim + action_dim, output_size=1, **variant['qf_kwargs'] ) target_qf2 = ConcatMlp( input_size=obs_dim + action_dim, output_size=1, **variant['qf_kwargs'] ) if rl_algo == 'td3': policy = TanhMlpPolicy( input_size=obs_dim, output_size=action_dim, **variant['policy_kwargs'] ) target_policy = TanhMlpPolicy( input_size=obs_dim, output_size=action_dim, **variant['policy_kwargs'] ) expl_policy = create_exploration_policy( env, policy, exploration_version=variant['exploration_type'], exploration_noise=variant['exploration_noise'], ) eval_policy = policy elif rl_algo == 'sac': policy = TanhGaussianPolicy( obs_dim=obs_dim, action_dim=action_dim, **variant['policy_kwargs'] ) expl_policy = policy eval_policy = MakeDeterministic(policy) post_process_mask_fn = partial( full_post_process_mask_fn, mask_conditioned=mask_conditioned, mask_variant=mask_variant, context_distrib=context_distrib, context_key=desired_goal_key, achieved_goal_key=achieved_goal_key, ) def context_from_obs_dict_fn(obs_dict): context_dict = { desired_goal_key: obs_dict[achieved_goal_key] } if mask_conditioned: sample_masks_for_relabeling = mask_variant.get('sample_masks_for_relabeling', True) if sample_masks_for_relabeling: batch_size = next(iter(obs_dict.values())).shape[0] sampled_contexts = context_distrib.sample(batch_size) for mask_key in mask_keys: context_dict[mask_key] = sampled_contexts[mask_key] else: for mask_key in mask_keys: context_dict[mask_key] = obs_dict[mask_key] return context_dict def concat_context_to_obs(batch, replay_buffer=None, obs_dict=None, next_obs_dict=None, new_contexts=None): obs = batch['observations'] next_obs = batch['next_observations'] batch_size = obs.shape[0] if mask_conditioned: if obs_dict is not None and new_contexts is not None: if not mask_variant.get('relabel_masks', True): for k in mask_keys: new_contexts[k] = next_obs_dict[k][:] batch.update(new_contexts) if not mask_variant.get('relabel_goals', True): new_contexts[desired_goal_key] = next_obs_dict[desired_goal_key][:] batch.update(new_contexts) new_contexts = post_process_mask_fn(obs_dict, new_contexts) batch.update(new_contexts) if mask_format in ['vector', 'matrix']: goal = batch[desired_goal_key] mask = batch['mask'].reshape((batch_size, -1)) batch['observations'] = np.concatenate([obs, goal, mask], axis=1) batch['next_observations'] = np.concatenate([next_obs, goal, mask], axis=1) elif mask_format == 'distribution': goal = batch[desired_goal_key] sigma_inv = batch['mask_sigma_inv'].reshape((batch_size, -1)) batch['observations'] = np.concatenate([obs, goal, sigma_inv], axis=1) batch['next_observations'] = np.concatenate([next_obs, goal, sigma_inv], axis=1) elif mask_format == 'cond_distribution': goal = batch[desired_goal_key] mu_w = batch['mask_mu_w'] mu_g = batch['mask_mu_g'] mu_A = batch['mask_mu_mat'] sigma_inv = batch['mask_sigma_inv'] if mask_variant['use_g_for_mean']: mu_w_given_g = goal else: mu_w_given_g = mu_w + np.squeeze(mu_A @ np.expand_dims(goal - mu_g, axis=-1), axis=-1) sigma_w_given_g_inv = sigma_inv.reshape((batch_size, -1)) batch['observations'] = np.concatenate([obs, mu_w_given_g, sigma_w_given_g_inv], axis=1) batch['next_observations'] = np.concatenate([next_obs, mu_w_given_g, sigma_w_given_g_inv], axis=1) else: raise NotImplementedError elif contextual_mdp: goal = batch[desired_goal_key] batch['observations'] = np.concatenate([obs, goal], axis=1) batch['next_observations'] = np.concatenate([next_obs, goal], axis=1) else: batch['observations'] = obs batch['next_observations'] = next_obs return batch if 'observation_keys' not in variant['contextual_replay_buffer_kwargs']: variant['contextual_replay_buffer_kwargs']['observation_keys'] = [] observation_keys = variant['contextual_replay_buffer_kwargs']['observation_keys'] if observation_key not in observation_keys: observation_keys.append(observation_key) if achieved_goal_key not in observation_keys: observation_keys.append(achieved_goal_key) replay_buffer = ContextualRelabelingReplayBuffer( env=env, context_keys=context_keys, context_distribution=context_distrib, sample_context_from_obs_dict_fn=context_from_obs_dict_fn, reward_fn=reward_fn, post_process_batch_fn=concat_context_to_obs, **variant['contextual_replay_buffer_kwargs'] ) if rl_algo == 'td3': trainer = TD3Trainer( policy=policy, qf1=qf1, qf2=qf2, target_qf1=target_qf1, target_qf2=target_qf2, target_policy=target_policy, **variant['td3_trainer_kwargs'] ) elif rl_algo == 'sac': trainer = SACTrainer( env=env, policy=policy, qf1=qf1, qf2=qf2, target_qf1=target_qf1, target_qf2=target_qf2, **variant['sac_trainer_kwargs'] ) def create_path_collector( env, policy, mode='expl', mask_kwargs={}, ): assert mode in ['expl', 'eval'] save_env_in_snapshot = variant.get('save_env_in_snapshot', True) if mask_conditioned: if 'rollout_mask_order' in mask_kwargs: rollout_mask_order = mask_kwargs['rollout_mask_order'] else: if mode == 'expl': rollout_mask_order = mask_variant.get('rollout_mask_order_for_expl', 'fixed') elif mode == 'eval': rollout_mask_order = mask_variant.get('rollout_mask_order_for_eval', 'fixed') else: raise TypeError if 'mask_distr' in mask_kwargs: mask_distr = mask_kwargs['mask_distr'] else: if mode == 'expl': mask_distr = mask_variant['expl_mask_distr'] elif mode == 'eval': mask_distr = mask_variant['eval_mask_distr'] else: raise TypeError if 'mask_ids' in mask_kwargs: mask_ids = mask_kwargs['mask_ids'] else: if mode == 'expl': mask_ids = mask_variant.get('mask_ids_for_expl', None) elif mode == 'eval': mask_ids = mask_variant.get('mask_ids_for_eval', None) else: raise TypeError prev_subtask_weight = mask_variant.get('prev_subtask_weight', None) max_subtasks_to_focus_on = mask_variant.get('max_subtasks_to_focus_on', None) max_subtasks_per_rollout = mask_variant.get('max_subtasks_per_rollout', None) mode = mask_variant.get('context_post_process_mode', None) if mode in ['dilute_prev_subtasks_uniform', 'dilute_prev_subtasks_fixed']: prev_subtask_weight = 0.5 return MaskPathCollector( env, policy, observation_key=observation_key, context_keys_for_policy=context_keys, concat_context_to_obs_fn=concat_context_to_obs, save_env_in_snapshot=save_env_in_snapshot, mask_sampler=(context_distrib if mode=='expl' else eval_context_distrib), mask_distr=mask_distr.copy(), mask_ids=mask_ids, max_path_length=max_path_length, rollout_mask_order=rollout_mask_order, prev_subtask_weight=prev_subtask_weight, max_subtasks_to_focus_on=max_subtasks_to_focus_on, max_subtasks_per_rollout=max_subtasks_per_rollout, ) elif contextual_mdp: return ContextualPathCollector( env, policy, observation_key=observation_key, context_keys_for_policy=context_keys, save_env_in_snapshot=save_env_in_snapshot, ) else: return ContextualPathCollector( env, policy, observation_key=observation_key, context_keys_for_policy=[], save_env_in_snapshot=save_env_in_snapshot, ) expl_path_collector = create_path_collector(env, expl_policy, mode='expl') eval_path_collector = create_path_collector(eval_env, eval_policy, mode='eval') algorithm = TorchBatchRLAlgorithm( trainer=trainer, exploration_env=env, evaluation_env=eval_env, exploration_data_collector=expl_path_collector, evaluation_data_collector=eval_path_collector, replay_buffer=replay_buffer, max_path_length=max_path_length, **variant['algo_kwargs'] ) algorithm.to(ptu.device) if variant.get("save_video", True): save_period = variant.get('save_video_period', 50) dump_video_kwargs = variant.get("dump_video_kwargs", dict()) dump_video_kwargs['horizon'] = max_path_length renderer = EnvRenderer(**variant.get('renderer_kwargs', {})) def add_images(env, state_distribution): state_env = env.env image_goal_distribution = AddImageDistribution( env=state_env, base_distribution=state_distribution, image_goal_key='image_desired_goal', renderer=renderer, ) img_env = InsertImagesEnv(state_env, renderers={ 'image_observation' : renderer, }) context_env = ContextualEnv( img_env, context_distribution=image_goal_distribution, reward_fn=reward_fn, observation_key=observation_key, update_env_info_fn=delete_info, ) return context_env img_eval_env = add_images(eval_env, eval_context_distrib) if variant.get('log_eval_video', True): video_path_collector = create_path_collector(img_eval_env, eval_policy, mode='eval') rollout_function = video_path_collector._rollout_fn eval_video_func = get_save_video_function( rollout_function, img_eval_env, eval_policy, tag="eval", imsize=variant['renderer_kwargs']['width'], image_format='CHW', save_video_period=save_period, **dump_video_kwargs ) algorithm.post_train_funcs.append(eval_video_func) # additional eval videos for mask conditioned case if mask_conditioned: default_list = [ 'atomic', 'atomic_seq', 'cumul_seq', 'full', ] eval_rollouts_for_videos = mask_variant.get('eval_rollouts_for_videos', default_list) for key in eval_rollouts_for_videos: assert key in default_list if 'cumul_seq' in eval_rollouts_for_videos: video_path_collector = create_path_collector( img_eval_env, eval_policy, mode='eval', mask_kwargs=dict( mask_distr=dict( cumul_seq=1.0 ), ), ) rollout_function = video_path_collector._rollout_fn eval_video_func = get_save_video_function( rollout_function, img_eval_env, eval_policy, tag="eval_cumul" if mask_conditioned else "eval", imsize=variant['renderer_kwargs']['width'], image_format='HWC', save_video_period=save_period, **dump_video_kwargs ) algorithm.post_train_funcs.append(eval_video_func) if 'full' in eval_rollouts_for_videos: video_path_collector = create_path_collector( img_eval_env, eval_policy, mode='eval', mask_kwargs=dict( mask_distr=dict( full=1.0 ), ), ) rollout_function = video_path_collector._rollout_fn eval_video_func = get_save_video_function( rollout_function, img_eval_env, eval_policy, tag="eval_full", imsize=variant['renderer_kwargs']['width'], image_format='HWC', save_video_period=save_period, **dump_video_kwargs ) algorithm.post_train_funcs.append(eval_video_func) if 'atomic_seq' in eval_rollouts_for_videos: video_path_collector = create_path_collector( img_eval_env, eval_policy, mode='eval', mask_kwargs=dict( mask_distr=dict( atomic_seq=1.0 ), ), ) rollout_function = video_path_collector._rollout_fn eval_video_func = get_save_video_function( rollout_function, img_eval_env, eval_policy, tag="eval_atomic", imsize=variant['renderer_kwargs']['width'], image_format='HWC', save_video_period=save_period, **dump_video_kwargs ) algorithm.post_train_funcs.append(eval_video_func) if variant.get('log_expl_video', True) and not variant['algo_kwargs'].get('eval_only', False): img_expl_env = add_images(env, context_distrib) video_path_collector = create_path_collector(img_expl_env, expl_policy, mode='expl') rollout_function = video_path_collector._rollout_fn expl_video_func = get_save_video_function( rollout_function, img_expl_env, expl_policy, tag="expl", imsize=variant['renderer_kwargs']['width'], image_format='CHW', save_video_period=save_period, **dump_video_kwargs ) algorithm.post_train_funcs.append(expl_video_func) addl_collectors = [] addl_log_prefixes = [] if mask_conditioned and mask_variant.get('log_mask_diagnostics', True): default_list = [ 'atomic', 'atomic_seq', 'cumul_seq', 'full', ] eval_rollouts_to_log = mask_variant.get('eval_rollouts_to_log', default_list) for key in eval_rollouts_to_log: assert key in default_list # atomic masks if 'atomic' in eval_rollouts_to_log: for mask_id in eval_path_collector.mask_ids: mask_kwargs=dict( mask_ids=[mask_id], mask_distr=dict( atomic=1.0, ), ) collector = create_path_collector(eval_env, eval_policy, mode='eval', mask_kwargs=mask_kwargs) addl_collectors.append(collector) addl_log_prefixes += [ 'mask_{}/'.format(''.join(str(mask_id))) for mask_id in eval_path_collector.mask_ids ] # full mask if 'full' in eval_rollouts_to_log: mask_kwargs=dict( mask_distr=dict( full=1.0, ), ) collector = create_path_collector(eval_env, eval_policy, mode='eval', mask_kwargs=mask_kwargs) addl_collectors.append(collector) addl_log_prefixes.append('mask_full/') # cumulative, sequential mask if 'cumul_seq' in eval_rollouts_to_log: mask_kwargs=dict( rollout_mask_order='fixed', mask_distr=dict( cumul_seq=1.0, ), ) collector = create_path_collector(eval_env, eval_policy, mode='eval', mask_kwargs=mask_kwargs) addl_collectors.append(collector) addl_log_prefixes.append('mask_cumul_seq/') # atomic, sequential mask if 'atomic_seq' in eval_rollouts_to_log: mask_kwargs=dict( rollout_mask_order='fixed', mask_distr=dict( atomic_seq=1.0, ), ) collector = create_path_collector(eval_env, eval_policy, mode='eval', mask_kwargs=mask_kwargs) addl_collectors.append(collector) addl_log_prefixes.append('mask_atomic_seq/') def get_mask_diagnostics(unused): from rlkit.core.logging import append_log, add_prefix, OrderedDict log = OrderedDict() for prefix, collector in zip(addl_log_prefixes, addl_collectors): paths = collector.collect_new_paths( max_path_length, variant['algo_kwargs']['num_eval_steps_per_epoch'], discard_incomplete_paths=True, ) old_path_info = eval_env.get_diagnostics(paths) keys_to_keep = [] for key in old_path_info.keys(): if ('env_infos' in key) and ('final' in key) and ('Mean' in key): keys_to_keep.append(key) path_info = OrderedDict() for key in keys_to_keep: path_info[key] = old_path_info[key] generic_info = add_prefix( path_info, prefix, ) append_log(log, generic_info) for collector in addl_collectors: collector.end_epoch(0) return log algorithm._eval_get_diag_fns.append(get_mask_diagnostics) if 'ckpt' in variant: from rlkit.misc.asset_loader import local_path_from_s3_or_local_path import os.path as osp assert variant['algo_kwargs'].get('eval_only', False) def update_networks(algo, epoch): if 'ckpt_epoch' in variant: return if epoch % algo._eval_epoch_freq == 0: filename = local_path_from_s3_or_local_path(osp.join(variant['ckpt'], 'itr_%d.pkl' % epoch)) print("Loading ckpt from", filename) data = torch.load(filename)#, map_location='cuda:1') eval_policy = data['evaluation/policy'] eval_policy.to(ptu.device) algo.eval_data_collector._policy = eval_policy for collector in addl_collectors: collector._policy = eval_policy algorithm.post_train_funcs.insert(0, update_networks) algorithm.train()